Hey everyone and welcome back.
Today we're gonna be taking a deep dive into a paper
that's all about making AI smarter.
Oh, very cool.
Yeah, it's called Mapping the Neurosymbolic AI Landscape
by Architectures.
Okay.
And essentially what it's doing is trying to figure out
how to blend the strengths of neural networks
and symbolic logic.
Okay, yeah.
So kind of two different approaches to AI
and this is kind of like a roadmap
for how to bring them together.
Yeah, it's really fascinating how this paper
is addressing kind of this core challenge
of how to combine those two things.
The power of like neural networks
and then the logic of symbolic reasoning.
And it's kind of like, think of it like this,
you know, carol networks are like these amazing artists
that can create these super realistic paintings,
but then they can't really explain, you know,
how they made the choices they did to create that.
Or like they can capture the what but not the why.
Exactly.
Yeah, they're good at recognizing patterns,
but they're not so good at explaining their reasoning.
And then on the other hand, you have symbolic logic,
which is like a master detective, you know.
Really good at connecting the dots
and explaining the steps of like their deduction.
But it can struggle when it's faced with,
you know, massive amounts of data or complex scenarios.
So this paper is saying,
what if we could combine the artist's intuition
with the detective's logic?
Exactly.
And that's where this idea of neurosymbolic AI comes in.
Okay, cool.
So the paper starts by kind of highlighting the limitations
of just relying on neural networks alone.
Okay.
And one of the big ones is structured reasoning.
So like neural networks can spot patterns and data,
but they struggle with understanding complex relationships
like cause and effect.
They might see a correlation,
but they can't really understand like
what the underlying cause is.
Exactly.
Yeah, it's like noticing that people carry umbrellas
when it's raining, but not understanding why.
Yeah.
Another limitation is like the huge amount
of data neural networks need to learn effectively.
Oh yeah.
They're data hungry beasts.
Yeah, for sure.
And in a lot of real world situations,
we just don't have tons and tons of labeled data
to feed them.
Right.
It's like if you were trying to teach somebody new language
and you just gave them a million random words,
but with no context.
Exactly.
Like no grammar, no nothing.
Yeah.
And that brings us to another point,
which is knowledge integration.
Okay.
You know, we humans come preloaded
with tons of knowledge about the world.
Right.
But it's surprisingly hard to like download that knowledge
into a neural network.
Yeah, even if we could get enough data sometimes,
just getting that data into the form
that the neural network can use
is a whole other challenge.
Exactly.
Yeah.
And then the next limitation is probably the one
that people are most familiar with,
which is explainability.
Okay.
Neural networks are often seen as these black boxes.
You know, they give us answers,
but we don't always know how they got to those answers.
Right.
And especially in like healthcare or finance
or you know, areas where you need to understand
the reasoning.
Exactly.
Like you can't just trust the black box.
Exactly.
We need AI that we can understand and trust.
Right.
And then finally in neural networks,
they don't offer guarantees.
Okay.
So they're probabilistic,
meaning there's always a chance they'll make a mistake.
So there's always a little bit of uncertainty,
even if they're incredibly accurate.
Right.
And that's a problem for things like
safety critical applications.
Right.
Like self-driving cars.
Of course.
Or you know, medical diagnosis.
Even a small error can have huge consequences.
So we need AI that's not just accurate,
but also reliable.
Exactly.
And that's where this paper gets really interesting.
Right.
Because it actually lays out all these different
neuro symbolic AI approaches.
Right.
That researchers are using to overcome these limitations.
Okay.
And these approaches generally fall into two main categories.
Composite frameworks and monolithic frameworks.
Okay. Let's unpack those terms.
What are composite frameworks?
So composite frameworks,
you can kind of think of it as like a team effort.
So the neural network and the symbolic logic components,
they work together,
but they each maintain their separate identities.
So they're cooperating, but not merging.
Exactly. Yeah.
It's like two experts collaborating on a project,
bringing their unique skills to the table.
Yeah.
And within composite frameworks,
there's kind of two main types.
Okay.
Direct supervision and indirect supervision.
Indirect supervision, the symbolic logic component,
acts as a guide or coach for the neural network
during training.
So it's like a tutor to help the neural network learn.
Exactly.
Yeah.
It can provide feedback or enforce constraints
or even inject expert knowledge
to help it learn better and prevent it
from making nonsensical predictions.
That makes sense.
Yeah.
So what about indirect supervision?
And how does that work?
So in indirect supervision,
the neural network and symbolic logic,
they work in a more sequential way.
Okay.
So the neural network focuses on pattern recognition,
like identifying objects in an image.
And then it passes that information to a logic model,
which uses its reasoning abilities
to make the final decision.
So the neural network is like the eyes
and the logic model is the brain.
Exactly.
Yeah. It's a division of labor.
Okay. Cool.
So within these categories,
the paper dives into a bunch of specific frameworks.
For example, there's one called Concordia,
which is this really unique composite framework
because it uses this three-way connection
between the neural and logical components.
Three, that sounds interesting.
Tell me more.
So first it allows the neural network's outputs
to be used as priors for the logical model.
Okay.
So it's kind of like the neural network
is whispering suggestions to the logic model
based on what it sees in the data.
So it's getting a head start.
Exactly.
Based on what the neural network is seeing.
Exactly.
And second, Concordia uses the logic model
to inject expert knowledge into the neural network
during training.
Okay. So it's like having a fact checker built in.
Exactly.
And then third, Concordia uses something called
a gating network to kind of dynamically combine
the predictions of the neural and logical models
during inference.
Okay. So it's like a smart switchboard
picking the best option.
Exactly. Yeah.
It's like choosing the most reliable output
based on the specific input.
That's pretty clever.
Yeah.
So what about the other type of framework,
the monolithic ones?
Right. So hashtag TTS, the deep dive episode 2024,
1030, part two of three.
So monolithic frameworks, they take a different approach.
Okay.
Instead of having these separate neural
and symbolic components, they try to weave logic
directly into the structure of the neural network itself.
So it's less like a collaboration
and more like merging their brains into one.
Exactly.
It's like a much tighter integration.
Sure.
The paper breaks these down into two types.
Okay.
Logically wired neural networks
and tenserized logic programs.
Logically wired neural networks sounds cool.
Let's start there.
Okay. So the idea is to create this direct mapping
between logical statements and the neurons in the network.
Okay.
So if a neuron is activated, it literally means
that a particular logical statement is true.
And the connections between neurons
mirror the logical relationships between those statements.
Wow. So the network structure itself represents the logic.
Exactly.
That's pretty wild.
Are there any examples of this in the paper?
Yeah, there are.
So one early example is K-Ban.
Coming in.
Which stands for Knowledge Based Artificial Neural Networks.
Okay.
And let's say we have a logical rule.
Like if it's raining and you're outside,
then you need an umbrella.
Okay, classic.
Right.
So in a K-Ban, there would be neurons that represent
each of those concepts raining outside and need umbrella.
Okay.
And the connections between these neurons
would be set up to reflect that rule.
Okay.
So if the raining and outside neurons are activated,
then the need umbrella neuron will also be activated.
So you're basically like physically wiring
the logic into the network.
Exactly.
And that makes it way easier to understand
like why the network is making a decision.
Exactly.
Yeah, that's one of the big advantages
of these logically wired networks
is this inherent explainability.
Right.
Because you can actually see like what led to what.
Right.
You can trace back through the network
and see, okay, which logical rules led to this output.
That's a huge step forward in terms of trust
and transparency.
Yeah, exactly.
But I imagine there are some limitations.
Yeah, of course.
So scalability is a big one.
Okay.
As the number of logical statements
and relationships grows.
Right.
The network can become super complex
and computationally expensive to train and use.
So it's like trying to manage a city's traffic flow
with a hand drawn map.
Yeah, exactly.
It works for small towns,
but it gets overwhelming for big cities.
Yeah, that's a good analogy.
Another challenge is that real world data is often messy
and doesn't always fit neatly
into those predefined logical rules.
So it's not quite as good at handling like nuance.
Exactly.
Okay, so what about tenserized logic programs,
the other type of monolithic framework?
Right, so this approach gets a little bit more mathematical.
I'm right.
But the core idea is to turn logical concepts
into mathematical objects.
Okay.
That neural networks can work with.
So instead of representing those logical statements
as activated neurons,
we represent them as matrices or tensors.
So you're translating the language of logic
into like the language of math.
Exactly.
Okay, but how does that actually work?
So one example is tensor log.
Tenser log.
Which compiles logical queries
into these things called differentiable functions.
Okay.
And essentially what that means is that we can use calculus
to optimize these logical queries.
Okay.
Just like we use calculus to train neural networks.
So you're kind of like blending logic and math
in a way that lets you harness the learning power
of neural networks.
Exactly.
Okay, that sounds powerful.
Are there any downsides?
So tensor log is known for being
both efficient and transparent.
So we can perform complex logical reasoning
without sacrificing speed or our ability
to understand how the system is working.
So we get the best of both world speed and explainability.
Exactly, but like all approaches, there are limitations.
Of course.
So tensor log works best with logical relationships
that are kind of chain like where one conclusion leads
to another in a very straightforward way.
Okay.
It might not be as effective for reasoning
that involves really complex interconnected relationships.
So it works great for certain types of logic,
but not a universal solution.
Exactly.
Okay, cool.
So we've explored both composite
and monolithic frameworks.
What are your thoughts on the overall potential
of this neuro symbolic AI?
Hashtag, TTS, the deep dive episode,
2024, 1030, part three of three.
I'm really starting to see the potential here.
Yeah.
It's like we're taking the best of both worlds,
combining this like intuition and learning of neural networks
with the reasoning of symbolic AI.
Yeah, definitely.
So it's not all just theory though, right?
What are some of like the real world areas
where you see this stuff making a difference?
Yeah, I mean the applications are huge.
One area is robotics.
So you can imagine robots that can not only
perform tasks efficiently,
but actually understand the reasons behind those tasks.
So they could like adapt and make decisions
based on like a deeper understanding of what's happening.
Exactly.
Yeah.
Neurosymbolic AI could enable robots to learn much faster,
generalized to new situations better,
and even collaborate more intuitively with humans.
That would be huge for so many industries,
like manufacturing healthcare space exploration.
Oh yeah, for sure.
What about healthcare?
I feel like there's a lot of potential there too.
Oh, absolutely.
Huge potential there.
So imagine AI systems that can analyze medical images,
lab results, patient histories,
to provide more accurate and explainable diagnoses.
That would be incredible.
And not just for the doctors,
but also like for the patients to build trust.
Exactly.
With medical professionals.
Yeah, and it goes beyond just diagnosis.
You know, neurosymbolic AI could help
personalize treatment plans, predict health risks,
even assist in drug discovery.
Wow.
Yeah.
What about education?
Do you see applications there?
Oh yeah, definitely.
So imagine like AI tutors that can adapt
to each student's individual learning style,
identify their strengths and weaknesses,
and then provide targeted feedback and support.
That would be amazing.
Yeah.
They could totally revolutionize the way we learn.
For sure.
Make it more engaging, more effective and accessible.
Exactly.
And what about scientific discovery?
Do you think this could help accelerate research?
Oh yeah, I think it definitely could.
So, you know, by combining that data analysis
with logical reasoning,
it could help scientists identify patterns,
generate hypotheses, design experiments more effectively,
and even lead to discoveries
that we haven't even imagined yet.
That's so cool.
It really feels like we're on the edge of a new era of AI.
One that's more powerful, more understandable,
more aligned with human values.
I agree. I think this paper gives us
a really good look into that future.
Yeah, and it highlights all the awesome progress
that's being made in this field.
Definitely.
So to all our listeners out there,
if you're interested in learning more
about Neurosymbolic AI,
I highly recommend checking out this paper.
Yeah, for sure.
And who knows, maybe you will be inspired
to contribute to this field.
The future of AI is full of possibilities,
and Neurosymbolic AI is leading the way.
Definitely.
Well, that wraps up our deep dive
into mapping the Neurosymbolic AI landscape.
Thanks for joining us.
Of course.
And we'll see you next time.
Later.